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Siddharth PrasadEmail: sprasad2 (at) cs (dot) cmu (dot) eduOffice: GHC 5113 CV, Google Scholar, dblp |
I am a final-year PhD student in the Computer Science Department at Carnegie Mellon University advised by Nina Balcan and Tuomas Sandholm. My research spans mechanism and market design, integer programming, machine learning, algorithm design, and their various interactions. I am most interested in developing synergies between algorithm+mechanism design and data-driven methods to help improve the efficiency (economic, run-time, etc.) of human-scale operations.
In Summer 2022 I worked on recommender systems at Google Research as a student researcher with Craig Boutilier and Martin Mladenov. I received a B.S. in math and computer science from Caltech in 2019.
Thesis proposal: Mechanism Design and Integer Programming in the Data Age. [pdf]
Committee: Nina Balcan, Tuomas Sandholm, Gérard Cornuéjols, Craig Boutilier, Peter Cramton
Research papers
- New Sequence-Independent Lifting Techniques for Cutting
Planes and When They
Induce Facets
with Ellen Vitercik, Nina Balcan, and Tuomas Sandholm
Best poster award (honorable mention) at MIP workshop, 2024
[arXiv] - Content Prompting: Modeling Content Provider Dynamics to
Improve User Welfare in Recommender
Ecosystems
with Martin Mladenov and Craig Boutilier
Oral presentation at RecSys Workshop on Causality, Counterfactuals, and Sequential Decision Making, 2023
[arXiv] [recsys workshop talk]
- Bicriteria Multidimensional Mechanism Design with Side
Information
with Nina Balcan and Tuomas Sandholm
Conference on Neural Information Processing Systems (NeurIPS), 2023
Presented at first Econometric Society Economics and AI+ML Conference (ESIF-AIML), 2024
Presented at Marketplace Innovation Workshop (MIW), 2023
[arXiv] [sigecom reading list] - Structural Analysis of Branch-and-Cut and the Learnability
of Gomory Mixed Integer Cuts
with Nina Balcan, Tuomas Sandholm, and Ellen Vitercik
Conference on Neural Information Processing Systems (NeurIPS), 2022
Oral presentation (top 2% of submissions)
[arXiv] [poster] [video] - Maximizing Revenue under Market Shrinkage and Market
Uncertainty
with Nina Balcan and Tuomas Sandholm
Conference on Neural Information Processing Systems (NeurIPS), 2022
[slides] [poster] [video] - Improved Sample
Complexity Bounds for Branch-and-Cut
with Nina Balcan, Tuomas Sandholm, and Ellen Vitercik
International Conference on Principles and Practice of Constraint Programming (CP), 2022
[arXiv] [slides] - Sample Complexity of Tree Search Configuration: Cutting Planes and
Beyond
with Nina Balcan, Tuomas Sandholm, and Ellen Vitercik
Conference on Neural Information Processing Systems (NeurIPS), 2021
Spotlight presentation (top 3% of submissions)
[arXiv] [slides] [video] - Learning Within an Instance for Designing High-Revenue Combinatorial
Auctions
with Nina Balcan and Tuomas Sandholm
International Joint Conference on Artificial Intelligence (IJCAI), 2021
[proceedings version] [slides] [video] - Efficient Algorithms for Learning
Revenue-Maximizing Two-Part Tariffs
with Nina Balcan and Tuomas Sandholm
International Joint Conference on Artificial Intelligence (IJCAI), 2020
[slides] [video] -
Incentive Compatible Active Learning
with Federico Echenique
Innovations in Theoretical Computer Science Conference (ITCS), 2020
[arXiv] [slides] [video] -
Learning Time Dependent Choice
with Zachary Chase
Innovations in Theoretical Computer Science Conference (ITCS), 2019
[arXiv] [slides]
![[poster]](slides/poster_gmi_cuts.png){kind=link}
![[poster]](slides/poster_market_shrinkage.png){kind=link}
Selected talks
- Tree Search Configuration: Cutting Planes and Beyond (INFORMS Annual Meeting 2022).
- Within-Instance Mechanism Design (INFORMS Annual Meeting 2022).
- Learning to Cut in Integer Programming (CMU Theory Lunch).
- Learning Across and Within Instances in Mechanism Design (CMU Theory Lunch).
Teaching
I was a teaching assistant for the following courses.- 10-422: Foundations of Learning, Game Theory, and Their Connections (CMU, Spring 2024).
- 15-780: Graduate Artificial Intelligence (CMU, Spring 2023).
- CS 151: Complexity Theory (Caltech, Spring 2019).
- CS/SS/Ec 149: Algorithmic Economics (Caltech, Winter 2019).
- CS 38: Introduction to Algorithms (Caltech, Spring 2017).
- CS 21: Decidability and Tractability (Caltech, Winter 2017).
- Ma 0: Transition to Mathematical Proofs (Caltech, Summer 2017 and Summer 2018).